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Data as an Experience: Business Teams Deserve Better Than the Status Quo
The Experience Gap in Data Tools
Data tools have historically been built for technical teams instead of the business users who make daily decisions. In Part 4 of his blog series, The Death and Rebirth of Data, Cameron Price reframed the future of analytics by asking a question the industry has ignored for decades: What does using data actually feel like for the business. Lili Marsh expanded on this in her response, emphasizing that meaning only matters when people can experience and understand the data in a natural way.
Across my work in marketing and as a project manager on data initiatives, I have seen how poor user experience leads to failed adoption, wasted investment, stalled digital programs, and outcomes that never reach customers. This blog explores why this keeps happening, real cases where the experience gap caused catastrophic business impact, and how Latttice finally gives business teams the data experience they deserve.
The Everyday Reality: Business Tools Deliver Great Experiences, but Data Tools Do Not
Business Tools
In marketing, sales, operations, and customer service, teams use tools that are:
  • Intuitive
  • Guided
  • Visual
  • Contextual
  • Easy to understand
  • Designed for non technical users
These tools have one thing in common: they are built around people.
Data Tools
Data tools, on the other hand, are frequently:
  • Confusing
  • Slow
  • Poorly aligned to workflows
  • Rigid
  • Difficult to trust
  • Hard to navigate
  • Designed for technical users, not business teams
This mismatch becomes painfully obvious the moment a business user logs in. Lili captured this well: a data tool without a human experience will never deliver value.
I have seen this exact challenge on nearly every data project I have managed, regardless of industry.
Why Data Projects Keep Falling Short: The Missing Experience Layer
Technical teams often build exceptional architecture:
Pipelines
Models
Catalogs
Data products
Definitions
Integrations
Yet adoption remains low. Dashboards look impressive but are rarely used. Reports circulate without clarity. Teams make decisions based on gut feel or Excel.
What is missing is the experience layer:
  • A guided understanding of meaning
  • A conversational interface
  • A workflow that mirrors real work
  • Co creation with the business
  • Trust built through transparency
  • Clear context behind every metric
  • A sense of confidence rather than fear
Studies repeatedly show that more than sixty percent of analytics projects fail to deliver business outcomes. The technology is not the problem. The experience is.
The Business Cost of Poor Data Experience
When business users cannot understand or trust the data, outcomes suffer immediately.
Marketing cannot personalize campaigns
Sales teams cannot understand performance patterns
Operations cannot optimize processes
Customer service cannot anticipate needs
Leadership cannot guide with confidence
Customers feel the impact of poor internal visibility
Experience failure becomes business failure. And the cost is enormous.
Real World Examples: When Poor Data Experience Broke Business Outcomes
To understand why the data experience matters, we can look to real examples where a lack of transparency, clarity, and usability caused major business damage.
These are not isolated incidents. They are symptoms of a global pattern.
Target Canada: A Data Experience Collapse That Cost Billions
When Target launched in Canada, they implemented a complex data and inventory system intended to power a modern retail footprint.
The reality was very different.
System outputs were inconsistent and incomplete
Store managers received inventory numbers they could not trust
Buyers were working with conflicting information
Replenishment did not align with real demand
Head office dashboards contradicted store level reality
Teams on the ground described the data environment as chaotic and unusable. They had no transparency into why the numbers looked the way they did or how to correct them.
The collapse in trust led to operational paralysis. Target ultimately exited Canada with a loss exceeding five billion dollars.

The root cause was not the technology. It was the lack of a transparent, trustworthy experience for business users.
Hershey's ERP and Analytics Rollout: When Insight Fails, Revenue Follows
Hershey invested more than one hundred fifty million dollars in a modern data and ERP system. The goal was efficiency, clarity, and better decision making.
Instead, the rollout created confusion:
Dashboards were unintuitive and difficult to interpret
Order processing data was inconsistent
Business teams did not understand the new metrics
System outputs did not reflect operational reality
The consequences were severe. During Halloween season, Hershey could not ship one hundred million dollars worth of Kisses, Reese's, and Jolly Ranchers.
This is what happens when a system is technically sound but experientially unusable.
The UK Post Office Horizon Scandal: The Human Cost of No Experience Layer
Horizon, the system used by thousands of UK postmasters, produced data discrepancies that users had no way to question, investigate, or understand.
The System Had:
  • No transparency
  • No visibility into how numbers were generated
  • No tools to explore or validate data
  • No experience for the people doing the work
The Consequences:
  • Wrongful prosecutions
  • Financial ruin for many families
  • Loss of livelihoods
  • Public inquiries
  • Billions in compensation
When discrepancies appeared, the system assumed the user was wrong. The consequences were devastating:

This remains one of the clearest examples in history of what happens when systems override experience and trust.
Healthcare Analytics: Platforms Bought but Never Adopted
Hospitals across the United States invested heavily in analytics tools intended to improve patient outcomes.
Yet adoption remained extremely low.
Clinicians reported:
Dashboards were too complex
Definitions were unclear
Tools did not match clinical workflows
Insight was too slow for real decisions
Interfaces were frustrating and time consuming
In many organizations, adoption was below twenty percent, despite millions invested.
When the experience does not match reality, adoption collapses.
Government Digitization: When Systems Are Delivered but Capability Does Not Improve
Audit offices in Australia and the United Kingdom found a consistent pattern across multiple government digitization programs:
01
Systems launched without proper user testing
02
Data flows did not reflect how departments actually work
03
Interfaces created confusion rather than clarity
04
Insights did not reach frontline decision makers
Technology was delivered. Business capability did not improve. The experience gap canceled out the investment.
The Pattern Behind Every Failure
Across all these cases, the same experience issues appear:
Business users could not understand the insight
Definitions lacked clarity
Systems produced outputs without transparency
Insight did not map to real workflows
Business users had no conversational or exploratory interface
Trust collapsed because the experience collapsed
When the experience fails, the entire strategy fails.
How Latttice Solves the Root Causes Behind These Failures

Latttice is designed to prevent the structural problems behind decades of failed data initiatives.

It does not patch over the past. It redefines the experience from the beginning.

1. Transparent, Governed Data Products Build Trust
Latttice provides:
  • Full lineage
  • Clear definitions
  • Consistent meaning
  • Real transparency
Teams trust the data because they can see and understand it. This directly addresses the trust breakdown seen in Target, Hershey, and healthcare.
2. Conversational, Real Time Insight Instead of Static Dashboards
With Latttice, users ask questions in plain language and receive instant responses. The system becomes a conversation, not a maze.

This solves the experience gaps that caused confusion in Hershey, healthcare, and government programs.
3. A Consumer Grade Experience Designed for Business Users
Latttice is intuitive, guided, and context rich.
It reflects how people actually work, removing the fear and friction that defined the failures in every case study.
4. No Unnecessary Data Movement or Replication
Latttice accesses data where it resides. Nothing is duplicated unless necessary.
This prevents version drift, system contradiction, and invisible inconsistencies. It directly addresses the breakdowns seen in Target and Horizon.
5. Insight That Reaches Customers and Improves Outcomes
When business teams finally have a seamless experience:
Marketing acts faster
Sales understands performance
Customer teams respond effectively
Product teams learn from patterns
Leadership makes decisions confidently
Better experience creates better business outcomes. That is the promise data has always made, and the promise Latttice finally fulfills.
If You Want Better Customer Outcomes, Start With a Better Data Experience
For decades, businesses have invested heavily in data programs that never delivered. Not because the teams were wrong. Not because the technology was weak.
But because the experience was never designed for the people who needed the insight.
Latttice changes that. It gives business users clarity, trust, and ownership. It creates a data experience that finally works.
If you want a data experience that empowers your teams and improves how your customers feel, visit latttice.com.
Join a data conversation,
Jessie Moelzer.
References
Target Canada
CBC News. "Why Target Failed in Canada." Fortune. "Inside Target's Canada Meltdown." Rotman School of Management Case Analysis.
Hershey's ERP Failure
CIO Magazine. "Hershey's $150M ERP Failure." ComputerWorld. "How a Failed ERP Implementation Disrupted Halloween."
UK Post Office Scandal
BBC News. "The Post Office Scandal Explained." UK Government Inquiry Reports 2024–2025.
Healthcare Analytics
Harvard Business Review. "Why Healthcare Analytics Still Struggles." NEJM Catalyst. Healthcare IT News.
Government Digitization
Australian National Audit Office. Digital Transformation Audits. Australian Government Digital Transformation Agency.
Data Project Failure Studies
DataScience PM. "Big Data Project Failure Study." SalesforceBen. "Why BI and Analytics Projects Fail." Dataversity. "Why 60 Percent of BI Initiatives Fail." Melbourne Business School. "Why Analytics and AI Projects Fail." Arxiv.org. "Why Data Mesh Implementations Collapse." Consultancy UK. "Two Thirds of Digital Transformation Projects Fail."